Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Ivan, Protsyuk"'
Autor:
Alexey V. Melnik, Yoshiki Vázquez-Baeza, Alexander A. Aksenov, Embriette Hyde, Andrew C. McAvoy, Mingxun Wang, Ricardo R. da Silva, Ivan Protsyuk, Jason V. Wu, Amina Bouslimani, Yan Wei Lim, Tal Luzzatto-Knaan, William Comstock, Robert A. Quinn, Richard Wong, Greg Humphrey, Gail Ackermann, Timothy Spivey, Sharon S. Brouha, Nuno Bandeira, Grace Y. Lin, Forest Rohwer, Douglas J. Conrad, Theodore Alexandrov, Rob Knight, Pieter C. Dorrestein, Neha Garg
Publikováno v:
mSystems, Vol 4, Iss 5 (2019)
ABSTRACT To visualize the personalized distributions of pathogens and chemical environments, including microbial metabolites, pharmaceuticals, and their metabolic products, within and between human lungs afflicted with cystic fibrosis (CF), we genera
Externí odkaz:
https://doaj.org/article/52f7a097d5b64c42bd3fbf8ea662a647
Autor:
Ivan Protsyuk, Martin G. Seneviratne, Andre Saraiva, Natalie Harris, Hugh Montgomery, Mustafa Suleyman, Xavier Glorot, Dominic King, Jack W. Rae, Clifton R. Baker, Alistair Connell, Suman V. Ravuri, Trevor Back, Clemens Meyer, Nenad Tomasev, Harry Askham, Michal Zielinski, Ruth M. Reeves, Joseph R. Ledsam, Shakir Mohamed, Thomas F. Osborne, Cian Hughes, Chris Laing, Alan Karthikesalingam, Valerio Magliulo, Anne Mottram, Christopher Nielson, Sebastien Baur, Julien Cornebise, Demis Hassabis, Geraint Rees
Publikováno v:
Nature Protocols. 16:2765-2787
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1547705a8f9b73ddd1995e08dd7f633d
https://doi.org/10.1038/s41596-021-00513-5
https://doi.org/10.1038/s41596-021-00513-5
Autor:
Robert A. Quinn, Mélissa Nothias-Esposito, Oriane Moyne, Neha Garg, Fernando Vargas, Hiroshi Tsugawa, Zdeněk Kameník, Bindesh Shrestha, Julia M. Gauglitz, Sven W. Meyer, Tam Dang, Pieter C. Dorrestein, Michael Witting, Abinesh Sarvepalli, Madeleine Ernst, Alexey Gurevich, Giorgis Isaac, Nikolas Kessler, Zheng Zhang, Robin Schmid, Marcus Ludwig, Ngoc Hung Nguyen, Johannes Rainer, Mingxun Wang, Theodore Alexandrov, Laura-Isobel McCall, Ivan Protsyuk, Audrey Le Gouellec, Pierre-Marie Allard, Sebastian Böcker, Alan K. Jarmusch, Ricardo Silva, Heiko Neuweger, Irina Koester, Ansgar Korf, Oliver Kohlbacher, Anupriya Tripathi, Daniel Petras, Kai Dührkop, Simon Rogers, Julien Paolini, Alexander A. Aksenov, Markus Fleischauer, Oliver Alka, Fabian Aicheler, Steffen Neumann, Kelly C. Weldon, Louis-Félix Nothias, Mustafa Morsy, Christian Martin H, Hosein Mohimani, Vanessa V. Phelan, Nuno Bandeira, Jonathan McSayles, Xavier Cachet, Justin J. J. van der Hooft, Kyo Bin Kang, Florian Zubeil, Tomáš Pluskal, Aiko Barsch, Heejung Yang, Andrés Mauricio Caraballo-Rodríguez
Publikováno v:
Nature methods, vol 17, iss 9
Nature methods
Nature Methods
Nature Methods, Nature Publishing Group, 2020, 17 (9), pp.905-908. ⟨10.1038/s41592-020-0933-6⟩
Nat. Methods 17, 905–908 (2020)
Nature Methods 17 (2020) 9
Nature Methods, 17(9), 905-908
Nature methods
Nature Methods
Nature Methods, Nature Publishing Group, 2020, 17 (9), pp.905-908. ⟨10.1038/s41592-020-0933-6⟩
Nat. Methods 17, 905–908 (2020)
Nature Methods 17 (2020) 9
Nature Methods, 17(9), 905-908
Feature-based molecular networking allows the generation of molecular networks for mass spectrometry data that can recognize isomers, incorporate relative quantification and integrate ion mobility data.Molecular networking has become a key method to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a31c0489fe436e78d33f542d30efe91
https://doi.org/10.1038/s41592-020-0933-6
https://doi.org/10.1038/s41592-020-0933-6
Autor:
Hugh Montgomery, Alan Karthikesalingam, Xavier Glorot, Christopher Nielson, Harry Askham, Suman V. Ravuri, Trevor Back, Joseph R. Ledsam, Michal Zielinski, Kelly S. Peterson, Geraint Rees, Alistair Connell, Nenad Tomasev, Julien Cornebise, Ivan Protsyuk, Andre Saraiva, Demis Hassabis, Cian Hughes, Chris Laing, Ruth M. Reeves, Shakir Mohamed, Dominic King, Anne Mottram, Jack W. Rae, Mustafa Suleyman, Clemens Meyer, Clifton R. Baker
Publikováno v:
Nature
The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3716eea45fa33fb37c38efb1a54cdd0
https://doi.org/10.1038/s41586-019-1390-1
https://doi.org/10.1038/s41586-019-1390-1
Autor:
Anne Mottram, Jessica Schrouff, Eric Loreaux, Martin G. Seneviratne, Diana Mincu, Nenad Tomasev, Ivan Protsyuk, Shaobo Hou, Alan Karthikesalingam, Sebastien Baur
Publikováno v:
CHIL
Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states. These deep, recurrent architectures have displayed increased performa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::401bf20c03b42922b13baa9a0f5f8887
https://doi.org/10.1145/3450439.3451858
https://doi.org/10.1145/3450439.3451858
Autor:
Alan Karthikesalingam, Eric Loreaux, Alistair Connell, Hugh Montgomery, Jessica Schrouff, Natalie Harris, Anne Mottram, Nenad Tomasev, Martin G. Seneviratne, Subhrajit Roy, Diana Mincu, Yuan Xue, Ivan Protsyuk
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
ObjectiveMultitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer – i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65dc9607e6aef28111d3ab70d51ea7e4
https://pubmed.ncbi.nlm.nih.gov/34151965
https://pubmed.ncbi.nlm.nih.gov/34151965
Autor:
Nenad, Tomašev, Natalie, Harris, Sebastien, Baur, Anne, Mottram, Xavier, Glorot, Jack W, Rae, Michal, Zielinski, Harry, Askham, Andre, Saraiva, Valerio, Magliulo, Clemens, Meyer, Suman, Ravuri, Ivan, Protsyuk, Alistair, Connell, Cían O, Hughes, Alan, Karthikesalingam, Julien, Cornebise, Hugh, Montgomery, Geraint, Rees, Chris, Laing, Clifton R, Baker, Thomas F, Osborne, Ruth, Reeves, Demis, Hassabis, Dominic, King, Mustafa, Suleyman, Trevor, Back, Christopher, Nielson, Martin G, Seneviratne, Joseph R, Ledsam, Shakir, Mohamed
Publikováno v:
Nature protocols. 16(6)
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::b6eee043792243fcb4afed951517d636
https://pubmed.ncbi.nlm.nih.gov/33953393
https://pubmed.ncbi.nlm.nih.gov/33953393
Autor:
James T. Morton, Alexey V. Melnik, Tal Luzzatto Knaan, Pieter C. Dorrestein, Theodore Alexandrov, Amina Bouslimani, Rob Knight, Ivan Protsyuk, Yoshiki Vázquez-Baeza, Clifford A. Kapono, Stefan Janssen, Larry Smarr, Neha Garg, Qiyun Zhu, Kayla Orlinsky
Publikováno v:
Scientific Reports, Vol 8, Iss 1, Pp 1-12 (2018)
Scientific Reports
Scientific reports, vol 8, iss 1
Kapono, CA; Morton, JT; Bouslimani, A; Melnik, AV; Orlinsky, K; Knaan, TL; et al.(2018). Creating a 3D microbial and chemical snapshot of a human habitat. SCIENTIFIC REPORTS, 8. doi: 10.1038/s41598-018-21541-4. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/9k1473hj
Scientific Reports
Scientific reports, vol 8, iss 1
Kapono, CA; Morton, JT; Bouslimani, A; Melnik, AV; Orlinsky, K; Knaan, TL; et al.(2018). Creating a 3D microbial and chemical snapshot of a human habitat. SCIENTIFIC REPORTS, 8. doi: 10.1038/s41598-018-21541-4. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/9k1473hj
One of the goals of forensic science is to identify individuals and their lifestyle by analyzing the trace signatures left behind in built environments. Here, microbiome and metabolomic methods were used to see how its occupants used an office and to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::011d6323136e7f6aec3518e946e23991
http://link.springer.com/article/10.1038/s41598-018-21541-4
http://link.springer.com/article/10.1038/s41598-018-21541-4
Autor:
Forest Rohwer, Pieter C. Dorrestein, Daniel Petras, Emma Ransome, Barbara A. Bailey, Mark J. A. Vermeij, Robert A. Quinn, Theodore Alexandrov, Ivan Protsyuk, Frederick I. Archer, Aaron C. Hartmann, Gareth J. Williams
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences of the United States of America, 114(44), 11685-11690. National Academy of Sciences
Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences of the United States of America, 114(44), 11685-11690. National Academy of Sciences
Significance Coral reef taxa produce a diverse array of molecules, some of which are important pharmaceuticals. To better understand how molecular diversity is generated on coral reefs, tandem mass spectrometry datasets of coral metabolomes were anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44a20c8978c2d7937777e1d982877653
https://doi.org/10.1073/pnas.1710248114
https://doi.org/10.1073/pnas.1710248114
Autor:
Rob Knight, Alexander A. Aksenov, Fernando Vargas, Ivan Protsyuk, Theodore Alexandrov, Ricardo Silva, Alexey V. Melnik, Pieter C. Dorrestein, Alan K. Jarmusch, Anupriya Tripathi, Embriette R. Hyde, Amina Bouslimani
Publikováno v:
Analytical Chemistry. 89:7549-7559
Increasing appreciation of the gut microbiome's role in health motivates understanding the molecular composition of human feces. To analyze such complex samples, we developed a platform coupling targeted and untargeted metabolomics. The approach is f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::344067d2c16d76e6de6024a886f8adbf
https://doi.org/10.1021/acs.analchem.7b01381
https://doi.org/10.1021/acs.analchem.7b01381